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Interpreting a seemingly simple function word like "or," "behind," or "more" can require logical, numerical, and relational reasoning. How are such words learned by children? Prior acquisition theories have often relied on positing a foundation of innate knowledge. Yet recent neural-network-based visual question answering models apparently can learn to use function words as part of answering questions about complex visual scenes. In this paper, we study what these models learn about function words, in the hope of better understanding how the meanings of these words can be learned by both models and children. We show that recurrent models trained on visually grounded language learn gradient semantics for function words requiring spatial and numerical reasoning. Furthermore, we find that these models can learn the meanings of logical connectives and and or without any prior knowledge of logical reasoning as well as early evidence that they are sensitive to alternative expressions when interpreting language. Finally, we show that word learning difficulty is dependent on the frequency of models' input. Our findings offer proof-of-concept evidence that it is possible to learn the nuanced interpretations of function words in a visually grounded context by using non-symbolic general statistical learning algorithms, without any prior knowledge of linguistic meaning.

The article focuses on the importance of clinical informatics in neonatal care, highlighting its potential to provide critical resources for clinicians. Topics include the specialized data needed for neonatal care, the challenges in transitioning from paper to electronic health records, and the impact of informatics on real-time patient management and research.

Idiopathic anaphylaxis (IA) remains a frustrating challenge for both patients and physicians. The aim of this paper is to focus on IA in pediatric ages and suggest possible diagnostic algorithms according to specific age ranges (infants, children, and adolescents). In fact, in a variable percentage of patients, despite extensive diagnostic tests, the cause of anaphylactic episodes cannot be identified. Moreover, the lack of a unanimous IA definition requires a careful and detailed diagnostic workup. Prompt recognition of signs and symptoms, especially in younger children, and an accurate clinical history often allow a choice of the most appropriate diagnostic tests and a correct differential diagnosis. [ABSTRACT FROM AUTHOR]

The vulnerable children refer to the special group of children with deviation in the process of children's psychological development and personality formation due to growth dilemmas. This may incur a series of serious social and family problems. The vulnerable children mainly cover the children suffering from children's psychological problems, such as childhood autism, autism, social anxiety and hypersensitivity, fear, depression, and PTSD arising from other factors. At present, the research results at home and abroad mainly focus on the psychological dynamic correlation investigation and solution discussion of a certain kind of difficult factor in the children's psychological development based on statistical data by the experimental methods, such as scale and model, and there is a blind spot in the humanistic orientation theory construction of psychological treatment for vulnerable children, causing the social reflection on children's psychological predicament from the humanistic perspective cannot be performed in related researches and going against searching for universal and integral theoretical paradigm for solving related problems. Sophisticated technologies for the observations have emerged increasingly for enabling the psychological features of vulnerable children through developmental cognitive neuroscience experiments. This paper introduces humanistic art therapy theory, focuses on the construction of a theoretical paradigm, and verifies its effectiveness based on the experimental results on the psychological development of vulnerable children, with an efficient performance. This study mainly refers to children with difficulty in social inclusion and psychological development. The results showed that two kinds of art therapy can obviously improve the psychological disorders of vulnerable children. The goal was to enhance self-cognition, strengthen emotional interaction, and implement positive motivation. [ABSTRACT FROM AUTHOR]

BACKGROUND: Active commuting to school can be a meaningful contributor to overall physical activity in children. To inform better micro-level urban design near schools that can support active commuting to school, there is a need for measures that capture these elements. This paper describes the adaptation of an observational instrument for use in assessing micro-scale environments around urban elementary schools in the United States. METHODS: The Micro-scale Audit of Pedestrian Streetscapes for Safe Routes to School (MAPS-SRTS) was developed from existing audit instruments not designed for school travel environments and modifications for the MAPS-SRTS instrument include the structure of the audit tool sections, the content, the observation route, and addition of new subscales. Subscales were analyzed for inter-rater reliability in a sample of 36 schools in Austin, TX. To assess reliability for each subscale, one-way random effects single-measure intraclass correlation coefficients (ICC) were used. RESULTS: Compared to the 30 original subscales, the adapted MAPS-SRTS included 26 (86.6%) subscales with revised scoring algorithms. Most MAPS-SRTS subscales had acceptable inter-rater reliability, with an ICC of 0.97 for the revised audit tool. CONCLUSIONS: The MAPS-SRTS audit tool is a reliable instrument for measuring the school travel environment for research and evaluation purposes, such as assessing human-scale determinants of active commuting to school behavior and documenting built environment changes from infrastructure interventions.

Background: This study aims to evaluate the performance of a deep learning system for the evaluation of tooth development stages on images obtained from panoramic radiographs from child patients. Methods: The study collected a total of 1500 images obtained from panoramic radiographs from child patients between the ages of 5 and 14 years. YOLOv5, a convolutional neural network (CNN)-based object detection model, was used to automatically detect the calcification states of teeth. Images obtained from panoramic radiographs from child patients were trained and tested in the YOLOv5 algorithm. True-positive (TP), false-positive (FP), and false-negative (FN) ratios were calculated. A confusion matrix was used to evaluate the performance of the model. Results: Among the 146 test group images with 1022 labels, there were 828 TPs, 308 FPs, and 1 FN. The sensitivity, precision, and F1-score values of the detection model of the tooth stage development model were 0.99, 0.72, and 0.84, respectively. Conclusions: In conclusion, utilizing a deep learning-based approach for the detection of dental development on pediatric panoramic radiographs may facilitate a precise evaluation of the chronological correlation between tooth development stages and age. This can help clinicians make treatment decisions and aid dentists in finding more accurate treatment options. [ABSTRACT FROM AUTHOR]

Background: Elevated rates of suicidal behavior were reported during the COVID-19 pandemic. However, information is scarce on patients' profiles during this period. Studies evoke the potential adverse effects of the mandatory lockdown, but they remain relatively speculative. Methods: We monitored fluctuations in suicide attempts (SA) in six European countries. We gathered data, retrospectively for under 18-year-old SA episodes (1 January 2018 to 31 December 2021), through records of psychiatric emergency services. We collected clinical profiles individually. We extracted environmental indicators by month, as provided by Oxford COVID-19 Government Response Tracker (OxCGRT). We used the Pruned Exact Linear Time (PELT) method to identify breakpoints in SA episodes reported for each country, and logistic regressions to estimate changes in patients' characteristics after the breakpoints. Finally, we used a univariate and multivariate negative binomial model to assess the link between SA and OxCGRT indicators, accounting for the delay (lag) between the interventions and their impact on SA. Results: The study comprised 2,833 children and adolescents (mean age = 15.1 years (SD 1.6); M: F sex-ratio = 1:5.4). A significant increase in SA was found either 6 or 10 months after the beginning of the pandemic, varying by country. Patients were more likely to be girls (aOR = 1.77 [1.34; 2.34]) and used SA methods "other than self-poisoning" (aOR = 1.34 [1.05; 1.7]). In the multivariate model, an association was found between SA and the contact tracing indicator with an 11 months delay, and the number of COVID-19 deaths with a 3-months delay. Conclusions: Findings confirmed a delayed increase in SA during the COVID-19 pandemic in children and adolescents as well as changes in patients' profiles. The duration and severity of the pandemic emerged as the strongest predictor in the rise of SA. If faced with a similar pandemic in the future, the gap between the onset of pandemic and the increase in suicide attempts presents an opportunity for prevention. [ABSTRACT FROM AUTHOR]

AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence of pubertal development. AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead ECGs. The first model was trained de novo using pediatric data. The second model used transfer learning from a previously validated adult data-derived algorithm. We analyzed the first ECG from 90,133 unique pediatric patients (aged ≤18 years) recorded between 1987–2022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0–7 years), peripubertal (8–14 years), and postpubertal (15–18 years) patients. The cohort was 46.7% male, with 21,678 prepubertal, 26,740 peripubertal, and 41,715 postpubertal children. The de novo pediatric model demonstrated 81% accuracy and an area under the curve (AUC) of 0.91. Model sensitivity was 0.79, specificity was 0.83, positive predicted value was 0.84, and the negative predicted value was 0.78, for the entire test cohort. The model's discriminatory ability was highest in postpubertal (AUC = 0.98), lower in the peripubertal age group (AUC = 0.91), and poor in the prepubertal age group (AUC = 0.67). There was no significant performance difference observed between the transfer learning and de novo models. AI-enabled interpretation of ECG can estimate sex in peripubertal and postpubertal children with high accuracy. [ABSTRACT FROM AUTHOR]

Background: Food insecurity significantly impacts children's health, affecting their development across cognitive, physical, and socio-emotional dimensions. This study explores the impact of food insecurity among children aged 6 months to 5 years, focusing on nutrient intake and its relationship with various forms of malnutrition. Methods: Utilizing machine learning algorithms, this study analyzed data from 819 children in the West Bank to investigate sociodemographic and health factors associated with food insecurity and its effects on nutritional status. The average age of the children was 33 months, with 52% boys and 48% girls. Results: The analysis revealed that 18.1% of children faced food insecurity, with household education, family income, locality, district, and age emerging as significant determinants. Children from food-insecure environments exhibited lower average weight, height, and mid-upper arm circumference compared to their food-secure counterparts, indicating a direct correlation between food insecurity and reduced nutritional and growth metrics. Moreover, the machine learning models observed vitamin B1 as a key indicator of all forms of malnutrition, alongside vitamin K1, vitamin A, and zinc. Specific nutrients like choline in the "underweight" category and carbohydrates in the "wasting" category were identified as unique nutritional priorities. Conclusion: This study provides insights into the differential risks for growth issues among children, offering valuable information for targeted interventions and policymaking. [ABSTRACT FROM AUTHOR]

Artificial intelligence has been applied to medical diagnosis and decision-making but it has not been used for classification of Class III malocclusions in children. Objective: This study aims to propose an innovative machine learning (ML)-based diagnostic model for automatically classifies dental, skeletal and functional Class III malocclusions. Methods: The collected data related to 46 cephalometric feature measurements from 4–14-year-old children (n = 666). The data set was divided into a training set and a test set in a 7:3 ratio. Initially, we employed the Recursive Feature Elimination (RFE) algorithm to filter the 46 input parameters, selecting 14 significant features. Subsequently, we constructed 10 ML models and trained these models using the 14 significant features from the training set through ten-fold cross-validation, and evaluated the models' average accuracy in test set. Finally, we conducted an interpretability analysis of the optimal model using the ML model interpretability tool SHapley Additive exPlanations (SHAP). Results: The top five models ranked by their area under the curve (AUC) values were: GPR (0.879), RBF SVM (0.876), QDA (0.876), Linear SVM (0.875) and L2 logistic (0.869). The DeLong test showed no statistical difference between GPR and the other models (p > 0.05). Therefore GPR was selected as the optimal model. The SHAP feature importance plot revealed that he top five features were SN-GoMe (the ratio of the length of the anterior skull base SN to that of the mandibular base GoMe), U1-NA (maxillary incisor angulation to NA plane), Overjet (the distance between two lines perpendicular to the functional occlusal plane from U1 and L), ANB (the difference between angles SNA and SNB), and AB-NPo (the angle between the AB and N-Pog line). Conclusions: Our findings suggest that ML models based on cephalometric data could effectively assist dentists to classify dental, functional and skeletal Class III malocclusions in children. In addition, features such as SN_GoMe, U1_NA and Overjet can as important indicators for predicting the severity of Class III malocclusions. [ABSTRACT FROM AUTHOR]

Food insecurity is a public health concern that affects children worldwide, yet it represents a particular burden for low- and middle-income countries. This study aims to utilize machine learning to identify the associations between food insecurity and nutrient intake among children aged 5 to 18 years. The study's sample encompassed 1040 participants selected from a 2022 food insecurity household conducted in the West Bank, Palestine. The results indicated that food insecurity was significantly associated with dietary nutrient intake and sociodemographic factors, such as age, gender, income, and location. Indeed, 18.2% of the children were found to be food-insecure. A significant correlation was evidenced between inadequate consumption of various nutrients below the recommended dietary allowance and food insecurity. Specifically, insufficient protein, vitamin C, fiber, vitamin B12, vitamin B5, vitamin A, vitamin B1, manganese, and copper intake were found to have the highest rates of food insecurity. In addition, children residing in refugee camps experienced significantly higher rates of food insecurity. The findings emphasize the multilayered nature of food insecurity and its impact on children, emphasizing the need for personalized interventions addressing nutrient deficiencies and socioeconomic factors to improve children's health and well-being. [ABSTRACT FROM AUTHOR]

To investigate the association between birth weight to placental weight (BW/PW) ratio, and echocardiographic left ventricle (LV) morphology at birth, while accounting for other relevant perinatal factors. A prospective cohort study was conducted on neonates at NewYork-Presbyterian Brooklyn Methodist Hospital from 2014 to 2018, categorized by their BW/PW percentile. Missing data were imputed with principal component analysis. Chi-squared and one-way analysis of variance were used to compare BW/PW groups and the best regression model was selected using a genetic and backward stepwise algorithm. We analyzed 827 neonates in three BW/PW groups: small (n=16), normal (n=488), and large (n=323). Placental thickness and smallest diameter were positively correlated with several LV parameters, including inter-ventricular septal thickness during diastole (IVSd) (p=0.002, p<0.001) and systole (IVSs) (p=0.001, p<0.001), LV posterior wall thickness at end of diastole (LVPWd) (p=0.003, p<0.001) and systole (LVPWs) (p<0.001, p<0.001), LV mass (p=0.017, p<0.001), and LV mass/volume (p=0.011, p<0.001). The BW/PW ratio correlated with an increased shortening fraction (estimate=0.29, 95 % CI 0.03–0.55, p=0.027). PW correlated with IVSs (p=0.019), while the longest placental diameter was linked to a decrease in LV internal dimension during diastole (LVIDd) (estimate=−0.07, p=0.039), LV mass (estimate=−0.11, p=0.024), and LV mass/volume (estimate=−0.55, p=0.005). This study found that several placental factors, including the BW/PW ratio, can independently affect LV dimension and morphology, highlighting the importance of fetal growth and placental health in the physiological adaptation of the fetal heart. More research is needed to establish causation and inform newborn prevention strategies. [ABSTRACT FROM AUTHOR]

Childhood obesity might threaten children's current and adulthood health outcomes. Previous studies have illustrated the positive effects of physical activity on weight control; however, there is a lack of evidence on the optimal dose of physical activity. Therefore, we aimed to explore the relationship between physical activity and overweight and obesity, as well as the optimal threshold for physical activity. The median (interquartile range) and number (proportion) were used to describe the statistics. The Mann–Whitney U test and chi-square test were used for an univariable analysis. The generalized additive model with a smooth function was used to depict the preliminary relationship between physical activity and overweight and obesity. The cut-off level of physical activity was identified using AddFor algorithms, and a logistic regression model was applied to explore the multivariable relationship between physical activity and overweight and obesity after adjusting for control variables. According to the statistical analyses, 24.3% of 842 children and adolescents had overweight and obesity. The average number of days that the participants engaged in more than one hour of physical activity was three days a week. The optimal cut-off level of physical activity for the prevention of childhood overweight and obesity was 4 times a week. The participants who exercised more than four times a week (AOR(95% CI) = 0.56(0.38, 0.83), p = 0.004) were less likely to be overweight and obese. In the context of the general lack of physical activity among children and adolescents, we recommend that children and adolescents engage in 60 min of physical activity more than four times a week for a healthy weight. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to develop an accessible stepwise management algorithm for pediatric presentations of occipital condyle fractures (OCFs) based on a systematic review of the published literature regarding diagnostic evaluation, treatment, and outcomes. Methods: A systematic review of the literature was conducted on PubMed to locate English language studies reporting on the management of pediatric OCFs. Data extraction of clinical presentation, management strategies, imaging, and treatment outcome was performed. Results: A total of 15 studies reporting on 38 patients aged 18 years and younger presenting with OCFs were identified. Loss of consciousness (LOC), depressed level of consciousness, neck pain, decreased neck range of motion (ROM), and cranial nerve injury were the most common presenting symptoms. Diagnostic imaging included radiographs, computed tomography (CT) scans, magnetic resonance imaging (MRI), and functional radiographs to assess cervical stability. Treatment options varied and included soft collar, hard collar, and halo vest. All studies resulted in a complete healing of the OCF, with resolution of associated pain. Conclusion: The proposed treatment algorithm suggests a framework for the management of pediatric OCFs based on the available evidence (levels of evidence: 3, 4). This review of the literature indicated that a stepwise approach should be utilized in the management of isolated pediatric OCFs. [ABSTRACT FROM AUTHOR]

Children with developmental central hypotonia have reduced muscle tone secondary to non-progressive damage to the brain or brainstem. Children may have transient delays, mild or global functional impairments, and the lack of a clear understanding of this diagnosis makes evaluating appropriate interventions challenging. This overview aimed to systematically describe the best available evidence for tools to identify and evaluate children with developmental central hypotonia aged 2 months to 6 years. A systematic review of systematic reviews or syntheses was conducted with electronic searches in PubMed, Medline, CINAHL, Scopus, Cochrane Database of Systematic Reviews, Google Scholar, and PEDro and supplemented with hand-searching. Methodological quality and risk-of-bias were evaluated, and included reviews and tools were compared and contrasted. Three systematic reviews, an evidence-based clinical assessment algorithm, three measurement protocols, and two additional measurement tools were identified. For children aged 2 months to 2 years, the Hammersmith Infant Neurological Examination has the strongest measurement properties and contains a subset of items that may be useful for quantifying the severity of hypotonia. For children aged 2–6 years, a clinical algorithm and individual tools provide guidance. Further research is required to develop and validate all evaluative tools for children with developmental central hypotonia. [ABSTRACT FROM AUTHOR]

The most common acute infection and leading cause of death in children worldwide is pneumonia. Clinical and laboratory tests essentially diagnose community-acquired pneumonia (CAP). CAP can be caused by bacteria, viruses, or atypical microorganisms. Imaging is usually reserved for children who do not respond to treatment, need hospitalisation, or have hospital-acquired pneumonia. This review discusses the imaging findings for acute CAP complications and the diagnostic role of each imaging modality. Pleural effusion, empyema, necrotizing pneumonia, abscess, pneumatocele, pleural fistulas, and paediatric acute respiratory distress syndrome (PARDS) are acute CAP complications. When evaluating complicated CAP patients, chest radiography, lung ultrasonography, computed tomography (CT), and magnetic resonance imaging (MRI) can be used, with each having their own pros and cons. Imaging is usually not needed for CAP diagnosis, but it is essential for complicated cases and follow-ups. Lung ultrasound can supplement chest radiography (CR), which starts the diagnostic algorithm. Contrast-enhanced computed tomography (CECT) is used for complex cases. Advances in MRI protocols make it a viable alternative for diagnosing CAP and its complications. [ABSTRACT FROM AUTHOR]

In recent years, advances in ultrasound therapeutics have been implemented into treatment algorithms for the adult population; however, the use of therapeutic ultrasound in the pediatric population still needs to be further elucidated. In order to better characterize the utilization and practicality of sonothrombolysis in the juvenile population, the authors conducted a literature review of current pediatric research in therapeutic ultrasound. The PubMed database was used to search for all clinical and preclinical studies detailing the use and applications of sonothrombolysis, with a focus on the pediatric population. As illustrated by various review articles, case studies, and original research, sonothrombolysis demonstrates efficacy and safety in clot dissolution in vitro and in animal studies, particularly when combined with microbubbles, with potential applications in conditions such as deep venous thrombosis, peripheral vascular disease, ischemic stroke, myocardial infarction, and pulmonary embolism. Although there is limited literature on the use of therapeutic ultrasound in children, mainly due to the lower prevalence of thrombotic events, sonothrombolysis shows potential as a noninvasive thrombolytic treatment. However, more pediatric sonothrombolysis research needs to be conducted to quantify the safety and ethical considerations specific to this vulnerable population. [ABSTRACT FROM AUTHOR]